Fuzzy sql for statistical databases
Download
1 / 14

FUZZY SQL FOR STATISTICAL DATABASES - PowerPoint PPT Presentation


  • 287 Views
  • Updated On :

FUZZY SQL FOR STATISTICAL DATABASES. Miroslav Hu d ec INFOSTAT – Bratislava MSIS 200 8. Introduction. Classical SQL and its disadvantages F uzzy improvement Generali sed “ where ” clause Case study for statistical database Conclusion. Classical SQL. select n, a 1 , a 2 ,…a n from T

Related searches for FUZZY SQL FOR STATISTICAL DATABASES

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'FUZZY SQL FOR STATISTICAL DATABASES' - payton


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Fuzzy sql for statistical databases l.jpg

FUZZY SQL FOR STATISTICAL DATABASES

Miroslav Hudec

INFOSTAT – Bratislava

MSIS 2008


Introduction l.jpg
Introduction

  • Classical SQL and its disadvantages

  • Fuzzyimprovement

  • Generalised“where”clause

  • Case studyfor statistical database

  • Conclusion


Classical sql l.jpg
Classical SQL

select n, a1, a2,…an

from T

where a1>A1 and a2<A2


Classical sql4 l.jpg
Classical SQL

select n, a1, a2,…an

from T

where a1>A1-p

and a2<A2+q


F uzzy improvement of the sql l.jpg
Fuzzy improvement of the SQL

  • Accesses relational databases in the unchanged structure

  • Supports queries based on linguistic expressions on the client side

    The query is modified as follows:

    select n, a1, a2,…an

    from T

    where a1 is Bigand a2 is Small


F uzzy sets for queries l.jpg
Fuzzy sets for queries

Big (greather than)Small (less than) Middle (equal,…)

___________ ______________________

The query: The query:The query:

select n, a1, a2,…anselect n, a1, a2,…anselect n, a1, a2,…an

from T from T from T

where a1:>=Ld where a1:<=Lgwhere a1>=Ld and a1<=Lg


Generalised logical condition l.jpg
Generalised logical condition

WHERE clause with fuzzy conditions only:

where n denotes number of attributes with fuzzy constraints in a WHERE clause of a query,

where and and or are fuzzy logical operators

where ai is a database attribute and L is the parameter of a fuzzy set

WHERE clause with fuzzy and classical constraints

[and/or][atribute_m LIKE “*String”][and/or] [atribute_l<Date]


Calculation of the qci l.jpg
Calculation of the QCI

The QCI values for selected records are calculated in next two steps:

1. Calculation of memebership degree to fuzzy sets

2. Calculation of query satisfaction:

- for logical And operator

min: i=1,...,n

- for logical Or operator

max: i=1,...,n


Example l.jpg
Example

selectdistrict, unemployment, area

fromT

whereunemployment is Big and area is Small.

Unemployment is described with„Big value“ fuzzy set andits parameters are: Ld=8% and Lp=10%.Areais represented with „Small value“ fuzzy set with parameters Lp=300km2and Lg=650km2.

The query has this form:

selectdistrict, unemployment, area

fromT

whereunemployment >8 and area <650





Conclusion l.jpg
Conclusion

This fuzzy approach supports work with linguistic expressions on the client side, nevertheless it does not change structure and processes onthe server side of relational databases.

Fuzzy improving of SQL queries has advantages in cases when the user can not unambiguously separate data he is interested in from data he is not interested in by sharp boundaries or when the user wants to obtain data that are very close to satisfy queries.In other cases classical SQL fulfils the requirements for data.

In further use the meaning of the query is not changed only shapes and boundaries of linguistic expressions are changed to catch new requests.

The state of art of this approach depends also on the theoretical and practical development of fuzzy database management systems.



ad